Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR data
Land surface temperature (LST) is an important indicator of global ecological environment and climate change. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the recently launched Sentinel-3 satellites provides high-quality observations for estimating global LST. The algorithm of the...
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| Format: | Article |
| Language: | English |
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Elsevier
2020-09-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0303243419313881 |
| _version_ | 1818549897238937600 |
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| author | Jiajia Yang Ji Zhou Frank-Michael Göttsche Zhiyong Long Jin Ma Ren Luo |
| author_facet | Jiajia Yang Ji Zhou Frank-Michael Göttsche Zhiyong Long Jin Ma Ren Luo |
| author_sort | Jiajia Yang |
| collection | DOAJ |
| description | Land surface temperature (LST) is an important indicator of global ecological environment and climate change. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the recently launched Sentinel-3 satellites provides high-quality observations for estimating global LST. The algorithm of the official SLSTR LST product is a split-window algorithm (SWA) that implicitly assumes and utilizes knowledge of land surface emissivity (LSE). The main objective of this study is to investigate alternative SLSTR LST retrieval algorithms with an explicit use of LSE. Seventeen widely accepted SWAs, which explicitly utilize LSE, were selected as candidate algorithms. First, the SWAs were trained using a comprehensive global simulation dataset. Then, using simulation data as well as in-situ LST, the SWAs were evaluated according to their sensitivity and accuracy: eleven algorithms showed good training accuracy and nine of them exhibited low sensitivity to uncertainties in LSE and column water vapor content. Evaluation based on two global simulation datasets and a regional simulation dataset showed that these nine SWAs had similar accuracy with negligible systematic errors and RMSEs lower than 1.0 K. Validation based on in-situ LST obtained for six sites further confirmed the similar accuracies of the SWAs, with the lowest RMSE ranges of 1.57–1.62 K and 0.49−0.61 K for Gobabeb and Lake Constance, respectively. While the best two SWAs usually yielded good accuracy, the official SLSTR LST generally had lower accuracy. The SWAs identified and described in this study may serve as alternative algorithms for retrieving LST products from SLSTR data. |
| first_indexed | 2024-12-12T08:39:15Z |
| format | Article |
| id | doaj.art-f74d418da4164ec2b1eef23fa02ab3dc |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-12-12T08:39:15Z |
| publishDate | 2020-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-f74d418da4164ec2b1eef23fa02ab3dc2022-12-22T00:30:50ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322020-09-0191102136Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR dataJiajia Yang0Ji Zhou1Frank-Michael Göttsche2Zhiyong Long3Jin Ma4Ren Luo5School of Resources and Environment, Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Resources and Environment, Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China; Corresponding author at: No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, Sichuan, China.Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, 76344, GermanyCollege of Meteorology and Oceanography, National University of Defense Technology, Nanjing, 211101, ChinaSchool of Resources and Environment, Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Resources and Environment, Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, ChinaLand surface temperature (LST) is an important indicator of global ecological environment and climate change. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the recently launched Sentinel-3 satellites provides high-quality observations for estimating global LST. The algorithm of the official SLSTR LST product is a split-window algorithm (SWA) that implicitly assumes and utilizes knowledge of land surface emissivity (LSE). The main objective of this study is to investigate alternative SLSTR LST retrieval algorithms with an explicit use of LSE. Seventeen widely accepted SWAs, which explicitly utilize LSE, were selected as candidate algorithms. First, the SWAs were trained using a comprehensive global simulation dataset. Then, using simulation data as well as in-situ LST, the SWAs were evaluated according to their sensitivity and accuracy: eleven algorithms showed good training accuracy and nine of them exhibited low sensitivity to uncertainties in LSE and column water vapor content. Evaluation based on two global simulation datasets and a regional simulation dataset showed that these nine SWAs had similar accuracy with negligible systematic errors and RMSEs lower than 1.0 K. Validation based on in-situ LST obtained for six sites further confirmed the similar accuracies of the SWAs, with the lowest RMSE ranges of 1.57–1.62 K and 0.49−0.61 K for Gobabeb and Lake Constance, respectively. While the best two SWAs usually yielded good accuracy, the official SLSTR LST generally had lower accuracy. The SWAs identified and described in this study may serve as alternative algorithms for retrieving LST products from SLSTR data.http://www.sciencedirect.com/science/article/pii/S0303243419313881Land surface temperature (LST)Split-window algorithm (SWA)Sentinel-3 SLSTRValidation |
| spellingShingle | Jiajia Yang Ji Zhou Frank-Michael Göttsche Zhiyong Long Jin Ma Ren Luo Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR data International Journal of Applied Earth Observations and Geoinformation Land surface temperature (LST) Split-window algorithm (SWA) Sentinel-3 SLSTR Validation |
| title | Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR data |
| title_full | Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR data |
| title_fullStr | Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR data |
| title_full_unstemmed | Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR data |
| title_short | Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR data |
| title_sort | investigation and validation of algorithms for estimating land surface temperature from sentinel 3 slstr data |
| topic | Land surface temperature (LST) Split-window algorithm (SWA) Sentinel-3 SLSTR Validation |
| url | http://www.sciencedirect.com/science/article/pii/S0303243419313881 |
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